HTN-MAKER: Learning HTNs with minimal additional knowledge engineering required

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2008, Pages 950-956

  Hogg, Chad ^a   Muñoz Avila, Héctor ^a   Kuter, Ugur ^b  

^a Lehigh University   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIONICS; INFORMATION THEORY; KNOWLEDGE ENGINEERING; LEARNING ALGORITHMS;

CLASSICAL PLANNING; DECOMPOSITION METHODS; DO-MAINS; HIERARCHICAL PLANNING; HTN PLANNING; INITIAL STATES; PLANNING DOMAINS; PLANNING PROBLEMS; SEMANTIC INFORMATIONS;

PLANNING;

EID: 57749179704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Bergmann, R., and Wilke, W. 1995. Building and refining abstract planning cases by change of representation language. Journal of Artificial Intelligence Research 53-118.
2. Botea, A.; Muller, M.; and Schaeffer, J. 2005. Learning partial-order macros from solutions. In Proc. of ICAPS '05. AAAI Press.
3. Erol, K.; Hendler, J.; and Nau, D. S. 1996. Complexity results for hierarchical task-network planning. Annals of Mathematics and Artificial Intelligence 18:69-93.
4. Fern, A.; Yoon, S. W.; and Givan, R. 2004. Learning domain-specific control knowledge from random walks. In Proc. of ICAPS '04. AAAI Press.
5. Ghallab, M.; Nau, D.; and Traverso, P. 2004. Automated Planning: Theory and Practice. Morgan Kauffmann.
6. Ilghami, O.; Muñoz-Avila, H.; Nau, D.; and Aha, D. W. 2005. Learning approximate preconditions for methods in hierarchical plans. In Proc. of ICML '05.
7. Knoblock, C. 1993. Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning. Norwell, MA: Kluwer Academic Publishers.
8. Langley, P., and Choi, D. 2006. Learning recursive control programs from problem solving. J. Mach. Learn. Res. 7:493-518.
9. Minton, S. 1998. Learning Effective Search Control Knowledge: an Explanation-Based Approach. Ph.D. Dissertation, Carnegie Mellon University.
10. Mitchell, T.; Keller, R.; and Kedar-Cabelli, S. 1986. Explanation-based generalization: A unifying view. Machine Learning 1.
11. Mooney, R. J. 1988. Generalizing the order of operators in macro-operators. Machine Learning 270-283.
12. Nau, D.; Cao, Y.; Lotem, A.; and Muñoz-Avila, H. 1999. SHOP: Simple hierarchical ordered planner. In Proc. of IJCAI '99, 968-973. AAAI Press.
13. Nau, D. S.; Au, T.-C.; Ilghami, O.; Kuter, U.; Muñoz-Avila, H.; Murdock, J. W.; Wu, D.; and Yaman, F. 2005. Applications of SHOP and SHOP2. IEEE Intelligent Systems 20(2):34-41.
14. Nejati, N.; Langley, P.; and Könik, T. 2006. Learning hierarchical task networks by observation. In Proc. of ICML '06, 665-672. New York, NY, USA: ACM.
15. Reddy, C., and Tadepalli, P. 1997. Learning goal-decomposition rules using exercises. In Proc. of ICML '97.
16. Ruby, D., and Kibler, D. F. 1991. SteppingStone: An empirical and analytic evaluation. In Proc. of AAAI '91, 527-531. Morgan Kaufmann.
17. Sacerdoti, E. D. 1975. The nonlinear nature of plans. In Proc. of IJCAI '75, 206-214.
18. Winner, E., and Veloso, M. M. 2003. DISTILL: Learning domain-specific planners by example. In Proc. of ICML '03.
19. Xu, K., and Muñoz-Avila, H. 2005. A domain-independent system for case-based task decomposition without domain theories. In Proc. of AAAI '05. AAAI Press.